Learning Deep Generative Models
published: Aug. 23, 2016, recorded: August 2016, views: 1326
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In this tutorial I will discuss mathematical basics of many popular deep generative models, including Restricted Boltzmann Machines (RBMs), Deep Boltzmann Machines (DBMs), Helmholtz Machines, Variational Autoencoders (VAE) and Importance Weighted Autoencoders (IWAE). I will further demonstrate that these models are capable of extracting meaningful representations from high-dimensional data with applications in visual object recognition, information retrieval, and natural language processing.
Download slides: deeplearning2016_salakhutdinov_generative_models_01.pdf (20.3 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !